Data-driven PID controller design for continuous stirred tank reactor

Since most chemical processes exhibit severe nonlinear and time-varying behavior, the control of such processes is challenging. In this paper, we propose data-driven controller design method based on lazy learning for chemical processes. Using a lazy learning algorithm, a local valid linear model denoting the current state of system is automatically exacted for adjusting the PID controller parameters based on input/output data. This scheme can adjust the PID parameters in an online manner even if the system has nonlinear properties. The simulation results on the dynamic model of Continuous Stirred Tank Reactor (CSTR) are provided to demonstrate the effectiveness of the proposed new control techniques.